The susceptibility to biases and discrimination is a pressing issue in today's labor markets. While digital recruitment systems play an increasingly significant role in human resource management, a systematic understanding of human-centered design principles for fair online hiring remains lacking, particularly considering the gap between idealized conceptualizations of fairness in research and actual fairness concerns expressed by job seekers. To address this gap, this work explores the potential of developing a fair recruitment framework based on job seekers' fairness concerns shared in r/jobs, one of the largest online job communities. Through a grounded theory approach, we uncover four overarching themes of job seekers' fairness concerns: personal attribute discrimination beyond legally protected attributes, interaction biases, improper interpretations of qualifications, and power imbalance. Drawing on value sensitive design, we derive design implications for fair algorithms and interfaces in recruitment systems, integrating them into a conceptual framework that spans different hiring stages.

翻译：当今劳动力市场中，偏见与歧视的易发性已成为紧迫问题。尽管数字化招聘系统在人力资源管理中的作用日益显著，但针对公平在线招聘的人本设计原则仍缺乏系统性理解，尤其考虑到研究中理想化的公平概念与求职者实际表达的公平关切之间存在差距。为弥合这一差距，本研究探索基于r/jobs（最大的在线求职社区之一）中求职者分享的公平关切构建公平招聘框架的潜力。通过扎根理论方法，我们揭示了求职者公平关切的四大核心主题：超越法律保护属性的个人特征歧视、互动偏见、资格认定的不当解读以及权力失衡。借鉴价值敏感设计理念，我们推导出招聘系统中公平算法与界面的设计启示，并将其整合为一个涵盖不同招聘阶段的概念框架。